EAERE Magazine - n.7 Fall 2019

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These dynamic dimensions raise a number of policy-relevant research questions, which I seek to address in Saussay (2019): are rigidities to gasoline consumption adaption heterogeneous across households? How does this affect household’s long term response to gasoline price increases? How does this potential dynamic heterogeneity affect the regressivity of gasoline price increases? To this end, I develop a simple dynamic model of household gasoline demand. Following the contributions of Scott (2012) and Filippini et al. (2018), I take inspiration from Becker et al. (1994) to develop a rational habits model of gasoline consumption. This model allows to capture the intertemporal dimension of gasoline demand through a parsimonious functional form linking present consumption to its past and future levels. Importantly, this paucity of data requirements makes it amenable to estimation on long-run household-level panel datasets. In practice I take advantage of the longest running household panel survey in the world, the Panel Study of Income Dynamics (PSID) in the U.S., which has included gasoline expenditure data biannually since 1999 2 . This yields a large panel of 6,074 U.S. households covering the years 1999 to 2015, a period marked by a high level of gasoline price variance. To complement this household-level data, I construct a localized gasoline price index stratified by state and county density using city and county-level data gathered by the C2ER 3 Cost of Living Index. Estimation results indicate that households experience rigidities in their gasoline consumption, with relatively strong price elasticities of -0.73 after two years and -0.88 in the long-term in the preferred specification. Importantly, I find evidence of dynamic heterogeneity among households. Rigidities in gasoline consumption are stronger on the bottom half of the income distribution, while conversely the top two income quintiles exhibit stronger forward-looking behavior. This contributes to a large heterogeneity of household responses: I find that households in the lowest income quintile are twice as sensitive to gasoline prices as their counterparts in the top one. Interestingly, I also find suggestive evidence of interactions between dynamic heterogeneity and the regressivity of gasoline price increases. Due to the greater inertia of their gasoline consumption, households in the bottom two quintiles of the income distribution experience a larger tax burden ratio after periods of falling gasoline prices. This implies that a gasoline tax implemented after a period of lenient prices would be more regressive. These findings have important policy implications. Beyond a confirmation of the heterogeneity of households’ responses to gasoline prices, these results demonstrate the existence and importance of dynamic heterogeneity. Households do not adjust their response at the same rate along the income distribution, which has an effect on the distributional impacts of increases in gasoline price. This makes the case for a reinforcement of compensatory policies targeting households on the lower end of the income distribution. 14
Endnotes 1 Sources: European Environmental Agency and U.S. Energy Information Agency 2 The PSID was conducted annually from 1968 until 1997, and biannually ever since. 3 Council for Community and Economic Research References Attanasio, O. P., Koujianou Goldberg, P., and Kyriazidou, E. (2008). Credit constraints in the market for consumer durables: Evidence from micro data on car loans. International Economic Review, 49(2):401- 436. Becker, G. S., Grossman, M., and Murphy, K. M. (1994). An empirical analysis of cigarette addiction. The American Economic Review, 84(3):396-418. Berry, A. (2019). The distributional effects of a carbon tax and its impact on fuel poverty: A microsimulation study in the french context. Energy Policy, 124:81-94. Blundell, R., Horowitz, J. L., and Parey, M. (2012). Measuring the price responsiveness of gasoline demand: Economic shape restrictions and nonparametric demand estimation. Quantitative Economics, 3(1):29-51. Combet, E., Ghersi, F., Hourcade, J.-C., and Thubin, C. (2010). La fiscalité carbone au risque des enjeux d’équité. Revue francaise d’economie, 25(2):59-91. Filippini, M., Hirl, B., and Masiero, G. (2018). Habits and rational behaviour in residential electricity demand. Resource and Energy Economics, 52:137-152. Mannering, F. and Winston, C. (1985). A dynamic empirical analysis of household vehicle ownership and utilization. The RAND Journal of Economics, pages 215-236. Metcalf, G. E. (1999). A distributional analysis of green tax reforms. National Tax Journal, pages 655- 681. Poterba, J. M. (1991). Is the gasoline tax regressive? Tax policy and the economy, 5:145-164. Ross, M. L., Hazlett, C., and Mahdavi, P. (2017). Global progress and backsliding on gasoline taxes and subsidies. Nature Energy, 2(1):16201. Saussay, A. (2019). Dynamic heterogeneity: rational habits and the heterogeneity of household responses to gasoline prices. Available at http://aureliensaussay.github.io/publication/gasoline/. Scott, K. R. (2012). Rational habits in gasoline demand. Energy Economics, 34(5):1713-1723. Sterner, T. (2007). Fuel taxes: An important instrument for climate policy. Energy Policy, 35(6):3194- 3202. Sterner, T. (2012). Distributional effects of taxing transport fuel. Energy Policy, 41:75-83. Wadud, Z., Noland, R. B., and Graham, D. J. (2010). A semiparametric model of household gasoline demand. Energy Economics, 32(1):93-101. West, S. E. (2004). Distributional effects of alternative vehicle pollution control policies. Journal of Public Economics, 88(3):735-757. West, S. E. and Williams, R. C. (2012). Estimates from a consumer demand system: implications for the incidence of environmental taxes. In Fuel Taxes and the Poor, pages 98-125. RFF Press. Yatchew, A. and No, J. A. (2001). Household gasoline demand in Canada. Econometrica, 69(6):1697-1709. 15
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EAERE Magazine - n.7 Fall 2019

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